Demographics and Descriptives

Environment

This section offers technical detail on the environment and data preparation. Non-technical readers should feel free to skip.

library(magrittr) # enables piping : %>%
library(ggplot2)
library(dplyr)
requireNamespace("tidyr")# data manipulation
requireNamespace("car")  # For it's `recode()` function.

source("./scripts/common-functions.R") # used in multiple reports
# source("./scripts/graph-presets.R") # fonts, colors, themes
source("./scripts/graphing/graph-missing.R")
baseSize = 8

config <- config::get()
#set default ggplot theme
ggplot2::theme_set(ggplot2::theme_bw())

describe_item <- function(d, varname){
  # browser()
  # d <- ds1
  # varname <- "Q2"
  variable_label <- ds_meta %>%
    dplyr::filter(q_name == varname ) %>%
    dplyr::pull(item_label)
  g <- d %>%
    TabularManifest::histogram_discrete(varname)+
    labs(
      title = paste0(varname," : ", variable_label)
    )
  return(g)
}

make_corr_matrix <- function(d,metaData,item_names){
  # d <- ds_opioid
  # metaData <- ds_meta
  # item_names <- c(q4_varnames,"total_opioid")
  #
  # browser()
  # d %>% glimpse()
  # d <- ds %>% dplyr::select(foc_01:foc_49)
  d1 <- d %>% dplyr::select(item_names)
  d2 <- d1[complete.cases(d1),] %>%
    dplyr::mutate(
      total_score = rowSums(.)
    )
  # d2 %>% glimpse()
  rownames <- metaData %>%
    dplyr::filter(q_name %in% item_names) %>%
    dplyr::mutate(display_name = paste0(q_name,"\n",q_label))

  rownames <- rownames[,"display_name"]
  rownames[nrow(rownames)+1,1]<- "total\nscore"
  rownames <- rownames %>% as.list() %>% unlist() %>% as.character()

  d3 <- sapply(d2, as.numeric)
  # d3 %>% glimpse()
  cormat <- cor(d3)
  colnames(cormat) <- rownames; rownames(cormat) <- rownames
  return(cormat)
}

make_corr_plot <- function (
  corr,
  lower="number",
  upper="circle",
  tl.pos=c("d","lt", "n"),
  diag=c("n", "l", "u"),
  bg="white",
  addgrid.col="gray", ...
){

  diag <- match.arg(diag)
  tl.pos <- match.arg(tl.pos)
  n <- nrow(corr)
  # corrplot::corrplot(corr, type="upper", method=upper, diag=TRUE, tl.pos=tl.pos, ...)
  corrplot::corrplot(corr, type="upper", method=upper, diag=TRUE, tl.pos=tl.pos)
  # corrplot::corrplot(corr, add=TRUE, type="lower", method=lower, diag=(diag == "l"), tl.pos="n", cl.pos="n", ...)
  corrplot::corrplot(corr, add=TRUE, type="lower", method=lower, diag=(diag == "l"), tl.pos="n", cl.pos="n")
  if (diag == "n" & tl.pos != "d") {
    symbols(1:n, n:1, add=TRUE, bg=bg, fg=addgrid.col,  inches=FALSE, squares=rep(1, n))
  }
}

# the production of the dto object is now complete
# we verify its structure and content:
ds0 <- readr::read_csv(config$oud_survey)
ds_meta <- readr::read_csv(config$survey_meta)

# recode into proper factors
# Q4
lvl_knowledge <- c(
   "Very knowledgeable"
  ,"Somewhat knowledgeable"
  ,"I have never heard of this treatment"
  ,"I choose not to answer"
)
# Q13, Q14
lvl_knowledge2 <- c(
  "Very knowledgeable"
  ,"Somewhat knowledgeable"
  ,"Not very knowledgeable"
  ,"Never heard of it"
  ,"I choose not to answer"
)
# Q6
lvl_helpful <- c(
    "Very helpful"
  ,"Somewhat helpful"
  ,"Neutral"
  ,"Not very helpful"
  ,"Not helpful at all"
  ,"Unsure"
  ,"I choose not to answer"
)

# Q11, Q12
lvl_common <- c(
  "Very common"
  ,"Somewhat common"
  ,"Rare"
  ,"None existent"
  ,"Unsure"
  ,"I choose not to answer"
)

# Q7, Q8, Q9, Q10
lvl_agreement <- c(
  "Strongly agree"
  ,"Somewhat agree"
  ,"Neutral"
  ,"Somewhat disagree"
  ,"Strongly disagree"
  ,"Unsure"
  ,"I choose not to answer"
)
# Q15
lvl_support <- c(
   "Strongly support"
  ,"Somewhat support"
  ,"Neutral/no opinion"
  ,"Somewhat oppose"
  ,"Strongly oppose"
  ,"Unsure"
  ,"I choose not to answer"
  ,"I do not know what this policy is/means"
)
#Q16
lvl_class_standing <- c(
  "Graduate student/Professional student"
  ,"Senior"
  ,"Junior"
  ,"Sophomore"
  ,"Freshman"
  ,"Non-degree seeking"
  ,"I choose not to answer"
)
lvl_age <- c(
   "61+ years old"
  ,"51-60 years old"
  ,"31-40 years old"
  ,"21-30 years old"
  ,"Under 20 years old"
)
lvl_gender <- c(
  "Female"
  ,"Male"
  ,"Other"
  ,"I choose not to answer"
)
lvl_political <- c(
   "Democrat"
  ,"Republican"
  ,"Independent/moderate"
  ,"Libertarian"
  ,"Very conservative"
  ,"Very liberal"
  ,"Somewhat conservative"
  ,"Somewhat liberal"
  ,"Other"
  ,"Unsure"
  ,"I choose not to answer"
)
lvl_religion <- c(
   "Very important"
  ,"Moderately important"
  ,"Not important"
  ,"I choose not to answer"
  ,"Unsure"
)
lvl_institution <- c(
  "Indiana University-Bloomington"
  ,"University of Central Florida"
  ,"Other"
)
ds1 <- ds0 %>%
  dplyr::mutate_at(vars(starts_with("Q4"))  ,~factor(.,levels = lvl_knowledge)) %>%
  dplyr::mutate_at(vars(starts_with("Q13")) ,~factor(.,levels = lvl_knowledge2)) %>%
  dplyr::mutate_at(vars(starts_with("Q14")) ,~factor(.,levels = lvl_knowledge2)) %>%
  dplyr::mutate_at(vars(starts_with("Q6"))  ,~factor(.,levels = lvl_helpful)) %>%
  dplyr::mutate_at(vars(starts_with("Q11")) ,~factor(.,levels = lvl_common)) %>%
  dplyr::mutate_at(vars(starts_with("Q12")) ,~factor(.,levels = lvl_common)) %>%
  dplyr::mutate_at(vars(starts_with("Q7"))  ,~factor(.,levels = lvl_agreement)) %>%
  dplyr::mutate_at(vars(starts_with("Q8"))  ,~factor(.,levels = lvl_agreement)) %>%
  dplyr::mutate_at(vars(starts_with("Q9"))  ,~factor(.,levels = lvl_agreement)) %>%
  dplyr::mutate_at(vars(starts_with("Q10")) ,~factor(.,levels = lvl_agreement)) %>%
  dplyr::mutate_at(vars(starts_with("Q15")) ,~factor(.,levels = lvl_support)) %>%
  dplyr::mutate(
     Q2 = factor(Q2, levels = lvl_institution)
    ,Q16 = factor(Q16, levels = lvl_class_standing)
    ,Q17 = factor(Q17, levels = lvl_age)
    ,Q19 = factor(Q19, levels = lvl_gender)
    ,Q20 = factor(Q20, levels = lvl_political)
    ,Q21 = factor(Q21, levels = lvl_religion)

  )
# too granular
# ds1 %>% group_by(Q5)  %>% count() # too granular to factorize, needs grouping
# ds1 %>% group_by(Q18) %>% count() # too granular to factorize, needs grouping
# ds1 %>% group_by(Q21) %>% count() # too granular to factorize, needs grouping
# ds1 %>% group_by(Q22) %>% count() # too granular to factorize, needs grouping
# ds1 %>% group_by(Q23) %>% count() # too granular to factorize, needs grouping
#
# ds1 %>% glimpse()

demographic <- c(
  "Q2"   = "institution"
  ,"Q16" = "class_standing"  # What is your class standing?
  ,"Q17" = "age"  # What is your age?
  ,"Q18" = "race"  # What best describes your race/ethnicity? Mark all that apply.
  ,"Q19" = "gender"  # What best describes your gender?
  ,"Q20" = "political"  # What best describes your political leanings?
  ,"Q21" = "religion"  # How important is religion or spirituality to you?
  ,"Q22" = "student_type"  # Mark all that apply to you.
  ,"Q23" = "field_of_study"  # Field of study (max = 2)?
)
methadone <- c(
  "Q7_1"  = "md_replace"
  ,"Q7_2" = "md_safe"
  ,"Q7_3" = "md_side_eff"
  ,"Q7_4" = "md_not_recov"
  ,"Q7_5" = "md_bad_phys"
  ,"Q7_6" = "md_get_high"
  ,"Q7_7" = "md_cravings"
  ,"Q7_8" = "md_from_high"
)
buprenorphine <- c(
  "Q8_1"  =  "br_replace"
  ,"Q8_2" =  "br_safe"
  ,"Q8_3" =  "br_side_eff"
  ,"Q8_4" =  "br_not_recov"
  ,"Q8_5" =  "br_bad_phys"
  ,"Q8_6" =  "br_get_high"
  ,"Q8_7" =  "br_cravings"
  ,"Q8_8" =  "br_from_high"
)
naltrexone <- c(
  "Q9_1"  = "nt_replace"
  ,"Q9_2" = "nt_safe"
  ,"Q9_3" = "nt_side_eff"
  ,"Q9_4" = "nt_not_recov"
  ,"Q9_5" = "nt_bad_phys"
  ,"Q9_6" = "nt_get_high"
  ,"Q9_7" = "nt_cravings"
  ,"Q9_8" = "nt_from_high"

)
varname_scale <- c(methadone, buprenorphine, naltrexone)
selected_varnames <- c(demographic, varname_scale)

# add renaming convention
ds_rename_guide <- tibble::tibble(
  q_name = selected_varnames %>% names()
  ,item_name = selected_varnames
)
ds_meta <- dplyr::left_join(
  ds_meta
  ,ds_rename_guide
  ,by = c("q_name" = "q_name")
) %>%
  dplyr::select(q_name, item_name, dplyr::everything())

Survey Response

This section demonstrates the attrition of survey responses due to data quality

cat("Initial responses, N = ", ds1 %>% n_distinct("ResponseId"))

Initial responses, N =  1439

ds1 %>% group_by(Status) %>% count() %>% neat()

Status	n
IP Address	1423
Spam	6
Survey Preview	10

ds2 <- ds1 %>% filter(Status == "IP Address")
cat("After keeping only `IP Address`\n",
    "Remaining responses, N =", ds2 %>% n_distinct("ResponseId"))

After keeping only `IP Address`
 Remaining responses, N = 1423

ds1 %>% group_by(Finished) %>% count() %>% neat()

Finished	n
FALSE	145
TRUE	1294

ds1 <- ds1 %>% filter(Finished)
cat("After keeping only those that finished the survey\n",
    "Remaining responses, N =", ds1 %>% n_distinct("ResponseId"))

After keeping only those that finished the survey
 Remaining responses, N = 1294

ds1 %>% group_by(UserLanguage) %>% count() %>% neat()

UserLanguage	n
EN	1294

ds_meta %>% filter(q_name == "Q1") %>% pull(item_label)

[1] "Are you 18 years old or older?"

ds1 %>% group_by(Q1) %>% count() %>% neat()

Q1	n
No	7
Yes	1286
NA	1

ds1 <- ds1 %>% filter(Q1 == "Yes")
cat("After keeping only those older than 18 years of age\n",
    "Remaining responses, N =", ds1 %>% n_distinct("ResponseId"))

After keeping only those older than 18 years of age
 Remaining responses, N = 1286

ds1 %>%
  mutate(
    date = lubridate::date(RecordedDate)
  ) %>%
  ggplot(aes(x = date) )+
  geom_bar()+
  labs(
    title = paste0("Date of response collection (N ="
                ,ds1 %>% n_distinct("ResponseId")
                , ")"
              )
    ,x = "2019"
    ,y = "Number of responses"
  )

d <- ds1 %>%
  # arrange(`Duration (in seconds)`) %>%
  mutate(
    minutes = `Duration (in seconds)`/60
    ,hours = `Duration (in seconds)`/60 / 60
    ,days = `Duration (in seconds)`/60 / 60 / 24
  ) %>%
  arrange(minutes) %>%
  select(minutes, hours, days) %>%
  dplyr::mutate(
    id = row_number()
  )
d %>% filter(hours > 1) %>%
  TabularManifest::histogram_continuous(
    "hours"
    ,bin_width = 1
    ,main_title = paste0(
      "Repondends who took more than 1 hour to complete the survey ( N = "
      ,d %>% filter(hours  > 1 ) %>% count() %>% pull(n), " )"
    )
  )

d %>% filter(hours < 1) %>%
  TabularManifest::histogram_continuous(
    "minutes"
    ,main_title = paste0(
      "Repondends who completed the survey within 1 hour ( N = "
      ,d %>% filter(hours <= 1) %>% count() %>% pull(n), " )"
    )
  )

ds2 <- ds1 %>% filter(`Duration (in seconds)` < 60*60 )
cat("After keeping only those who completed the survey within 1 hour\n",
    "Remaining responses, N =", ds2 %>% n_distinct("ResponseId"))

After keeping only those who completed the survey within 1 hour
 Remaining responses, N = 1248

Demographics

The following descriptives are based on N =  1248  observations.

 Q18:  What best describes your race/ethnicity? Mark all that apply. 

Q18	n
White/Caucasian	663
Hispanic/Latino	164
Black/African American	115
Asian	111
White/Caucasian,Hispanic/Latino	65
Multiracial	26
I choose not to respond	22
Other	15
Asian,White/Caucasian	7
Black/African American,Hispanic/Latino	7
American Indian/Alaskan Native	5
American Indian/Alaskan Native,White/Caucasian	5
NA	5
Black/African American,Other	4
Black/African American,White/Caucasian	4
Asian,White/Caucasian,Multiracial	3
Black/African American,Multiracial	3
Asian,Hispanic/Latino	2
Asian,Multiracial	2
White/Caucasian,Hispanic/Latino,Native Hawaiian/Pacific Islander	2
White/Caucasian,Other	2
American Indian/Alaskan Native,Asian	1
American Indian/Alaskan Native,Asian,Black/African American	1
American Indian/Alaskan Native,Hispanic/Latino,Multiracial	1
American Indian/Alaskan Native,Other	1
American Indian/Alaskan Native,White/Caucasian,Hispanic/Latino	1
Asian,Black/African American,Hispanic/Latino	1
Asian,White/Caucasian,Hispanic/Latino,Native Hawaiian/Pacific Islander,Multiracial	1
Black/African American,Hispanic/Latino,Multiracial	1
Black/African American,Native Hawaiian/Pacific Islander	1
Black/African American,White/Caucasian,Hispanic/Latino	1
Black/African American,White/Caucasian,Hispanic/Latino,Multiracial	1
Black/African American,White/Caucasian,Multiracial	1
Hispanic/Latino,Multiracial	1
White/Caucasian,Hispanic/Latino,Multiracial	1
White/Caucasian,I choose not to respond	1
White/Caucasian,Multiracial	1

 Q22:  Mark all that apply to you. 

Q22	n
I am a full-time student	561
I am a fraternity or sorority member,I am a full-time student	117
I am a transfer student from another college/university,I am a full-time student	100
I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	81
I am a commuter student,I am a full-time student	61
I am a transfer student from another college/university,I am a commuter student,I am a full-time student	35
I am an international student	29
I am a transfer student from another college/university	26
I am an international student,I am a full-time student	26
I am a fraternity or sorority member,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	21
I am an NCAA athlete,I am a full-time student	20
I am a fraternity or sorority member	17
I am a commuter student,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	16
I am a transfer student from another college/university,I am a fraternity or sorority member,I am a full-time student	15
I am an NCAA athlete	11
I am a transfer student from another college/university,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	9
I am a part-time student	8
I choose not to answer	8
NA	6
I am a commuter student	5
I am a transfer student from another college/university,I am a part-time student	5
I am a commuter student,I am a fraternity or sorority member,I am a full-time student	4
I am a transfer student from another college/university,I am an NCAA athlete,I am a full-time student	4
I am an active member in a registered, non-Greek (not fraternity or sorority) student organization	4
I am an international student,I am a transfer student from another college/university,I am a full-time student	4
I am an international student,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	4
I am a commuter student,I am a part-time student	3
I am a transfer student from another college/university,I am a commuter student,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	3
I am a veteran or active military member	3
I am a veteran or active military member,I am a full-time student	3
I am an international student,I am a transfer student from another college/university	3
I am an international student,I am an NCAA athlete,I am a full-time student	3
I am an NCAA athlete,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	3
I am a transfer student from another college/university,I am a commuter student,I am a fraternity or sorority member,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	2
I am an international student,I am a commuter student,I am a full-time student	2
I am an international student,I am a fraternity or sorority member	2
I am an NCAA athlete,I am a fraternity or sorority member,I am a full-time student	2
I am a commuter student,I am a fraternity or sorority member,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	1
I am a transfer student from another college/university,I am a commuter student,I am a full-time student,I am a part-time student	1
I am a transfer student from another college/university,I am a commuter student,I am a part-time student	1
I am a transfer student from another college/university,I am a full-time student,I am a part-time student	1
I am a transfer student from another college/university,I am a veteran or active military member,I am a commuter student,I am a full-time student	1
I am a transfer student from another college/university,I am a veteran or active military member,I am a commuter student,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a part-time student	1
I am a transfer student from another college/university,I am a veteran or active military member,I am a full-time student	1
I am a transfer student from another college/university,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization	1
I am a transfer student from another college/university,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a part-time student	1
I am a transfer student from another college/university,I am an NCAA athlete	1
I am a transfer student from another college/university,I am an NCAA athlete,I am a commuter student,I am a full-time student	1
I am a transfer student from another college/university,I am an NCAA athlete,I am a fraternity or sorority member,I am a part-time student	1
I am a transfer student from another college/university,I am an NCAA athlete,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization	1
I am a veteran or active military member,I am a commuter student,I am a full-time student	1
I am a veteran or active military member,I am a commuter student,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	1
I am a veteran or active military member,I am a part-time student	1
I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a part-time student	1
I am an international student,I am a fraternity or sorority member,I am a full-time student	1
I am an international student,I am a part-time student	1
I am an international student,I am a transfer student from another college/university,I am a commuter student,I am a full-time student	1
I am an international student,I am a transfer student from another college/university,I am an active member in a registered, non-Greek (not fraternity or sorority) student organization,I am a full-time student	1
I am an NCAA athlete,I am a commuter student,I am a full-time student	1

 Q23:  Which of the following best describe(s) your primary field of study (pick up to two only)? 

Q23	n
Psychology	110
Health sciences	56
Other	39
Biology	37
Finance	37
Business administration	36
Nursing	35
Biomedical science	34
Mechanical engineering	33
Computer science	31
Hospitality management	31
Aerospace engineering	29
Marketing	27
Accounting	21
Information technology	20
Elementary education	17
Economics	16
Journalism	15
Legal studies	15
Political science	15
Sport and exercise science	15
Advertising/public relations	14
Film	14
I choose not to answer	13
Management	13
Electrical engineering	12
Accounting,Finance	11
Communication sciences and disorders	10
Event management	10
Health services administration	10
Human communication	10
Entertainment management	9
Actuarial science	8
Chemistry	8
Civil engineering	8
Computer science,Electrical engineering	8
Digital media	8
Computer science,Information technology	7
Criminal justice,Psychology	7
Environmental studies	7
Industrial engineering	7
Accounting,Business administration	6
Art	6
Biology,Health sciences	6
Business administration,Marketing	6
Radio/television	6
Social work	6
Art,Film	5
Business administration,Finance	5
Criminal justice	5
Health sciences,Health services administration	5
Management,Marketing	5
Medical laboratory sciences	5
NA	5
Anthropology	4
Art,Digital media	4
Athletic training	4
Biomedical science,Psychology	4
Digital media,Film	4
Early childhood development and education	4
Environmental engineering	4
Event management,Hospitality management	4
Health sciences,Nursing	4
Interdisciplinary studies	4
Mathematics	4
Philosophy	4
Physics	4
Athletic training,Health sciences	3
Biology,Chemistry	3
Biology,Psychology	3
Business administration,Management	3
Criminal justice,Legal studies	3
Emerging media,Film	3
English	3
Film,Marketing	3
Forensic science	3
Health informatics and information management	3
Health sciences,Sport and exercise science	3
History,Information technology	3
Hospitality management,Restaurant and food service management	3
Nursing,Psychology	3
Philosophy,Political science	3
Psychology,Social work	3
Real estate	3
Sociology	3
Theater	3
Accounting,Film	2
Advertising/public relations,Digital media	2
Anthropology,Biology	2
Anthropology,History	2
Anthropology,Information technology	2
Architecture	2
Biology,Biomedical science	2
Biology,Nursing	2
Biology,Spanish	2
Biomedical science,Health sciences	2
Biomedical science,Other	2
Business administration,Information technology	2
Chemistry,Forensic science	2
Computer science,Digital media	2
Computer science,Mechanical engineering	2
Creative writing	2
Criminal justice,Sociology	2
Digital media,Radio/television	2
Entertainment management,Hospitality management	2
Health sciences,Psychology	2
Health sciences,Social work	2
History	2
Human communication,International and global studies	2
International and global studies,Journalism	2
Journalism,Theater	2
Legal studies,Political science	2
Marketing,Real estate	2
Music	2
Psychology,Sociology	2
Public administration	2
Statistics	2
Accounting,Actuarial science,Advertising/public relations,Aerospace engineering,Anthropology,Architecture	1
Accounting,Creative writing	1
Accounting,Environmental studies	1
Accounting,Information technology	1
Accounting,Marketing	1
Actuarial science,Biology	1
Actuarial science,Biomedical engineering/biotechnology	1
Actuarial science,Finance	1
Actuarial science,Psychology	1
Advertising/public relations,Business administration	1
Advertising/public relations,Communication sciences and disorders	1
Advertising/public relations,Economics	1
Advertising/public relations,Film	1
Advertising/public relations,Health informatics and information management	1
Advertising/public relations,Human communication	1
Advertising/public relations,Marketing	1
Aerospace engineering,Management	1
Aerospace engineering,Mechanical engineering	1
Anthropology,Biomedical science	1
Anthropology,English	1
Anthropology,Forensic science	1
Anthropology,French	1
Anthropology,Health sciences	1
Anthropology,Mathematics	1
Anthropology,Psychology	1
Architecture,Business administration	1
Architecture,Computer science	1
Architecture,Marketing	1
Art,Biology	1
Art,Computer science	1
Art,Event management	1
Art,History	1
Art,Marketing	1
Art,Other	1
Art,Political science	1
Art,Psychology	1
Art,Public administration	1
Art,Teacher education	1
Art,Theater	1
Athletic training,Sport and exercise science	1
Biology,Environmental studies	1
Biology,History	1
Biology,Medical laboratory sciences	1
Biology,Other	1
Biology,Statistics	1
Biomedical engineering/biotechnology,Other	1
Biomedical science,Chemistry	1
Biomedical science,Computer science	1
Biomedical science,Early childhood development and education	1
Biomedical science,Medical laboratory sciences	1
Biomedical science,Nursing	1
Business administration,Computer science	1
Business administration,Criminal justice	1
Business administration,Economics	1
Business administration,Event management	1
Business administration,Health services administration	1
Business administration,Hospitality management	1
Business administration,Humanities and cultural studies	1
Business administration,Journalism	1
Business administration,Legal studies	1
Business administration,Philosophy	1
Civil engineering,Construction engineering	1
Civil engineering,Sociology	1
Communication sciences and disorders,Health sciences	1
Communication sciences and disorders,Journalism	1
Computer science,English	1
Computer science,Mathematics	1
Computer science,Psychology	1
Construction engineering	1
Creative writing,Criminal justice	1
Creative writing,Film	1
Creative writing,Health sciences	1
Criminal justice,English	1
Criminal justice,Forensic science	1
Criminal justice,Philosophy	1
Criminal justice,Political science	1
Early childhood development and education,Elementary education	1
Early childhood development and education,Human communication	1
Early childhood development and education,Psychology	1
Early childhood development and education,Social work	1
Early childhood development and education,Theater	1
Economics,Information technology	1
Economics,Legal studies	1
Economics,Mathematics	1
Economics,Philosophy	1
Elementary education,Event management	1
Elementary education,Psychology	1
Emergency management	1
Emerging media,Radio/television	1
English,Psychology	1
English,Secondary education	1
English,Writing and rhetoric	1
Entertainment management,Environmental studies	1
Entertainment management,Music performance	1
Environmental studies,Other	1
Environmental studies,Political science	1
Event management,Restaurant and food service management	1
Film,Journalism	1
Film,Radio/television	1
Film,Spanish	1
Finance,Information technology	1
Finance,Management	1
Finance,Marketing	1
Finance,Other	1
French,International and global studies	1
Health sciences,Mathematics	1
Health services administration,Nursing	1
Health services administration,Public affairs	1
History,Secondary education	1
Hospitality management,Legal studies	1
Hospitality management,Marketing	1
Hospitality management,Social sciences	1
Human communication,Management	1
Human communication,Political science	1
Humanities and cultural studies	1
Humanities and cultural studies,Social work	1
Interdisciplinary studies,Social sciences	1
International and global studies	1
Journalism,Legal studies	1
Journalism,Writing and rhetoric	1
Legal studies,Other	1
Legal studies,Theater	1
Management,Music performance	1
Marketing,Social sciences	1
Marketing,Writing and rhetoric	1
Mechanical engineering,Other	1
Medical laboratory sciences,Sport and exercise science	1
Music education	1
Music,Political science	1
Nonprofit management	1
Nonprofit management,Public affairs	1
Nursing,Other	1
Photography,Psychology	1
Psychology,I choose not to answer	1
Psychology,Other	1
Public affairs	1
Public affairs,Other	1
Restaurant and food service management	1
Social sciences	1
Social work,Sociology	1
Spanish	1
Teacher education	1
Theater,Other	1

(Q4) Knowledge of Opioid Use

 SECTION Q4 
 Please mark your knowledge of the following treatments for opioid use disorder (i.e. opioid addiction).

 Number of complete cases =  1207

Prompt: Please mark your knowledge of the following treatments for opioid use disorder (i.e. opioid addiction).

Q4_1 Methadone

Q4_2 Buprenor

Q4_3 Naltrexone

Q4_4 PeerSupport

Q4_5 IndCounsel

Q4_6 GrpCounsel

Q4_7 OutpatientTx

Q4_8 ResidentTx

Q4_9 InpatientTx

(Q7) Methodone

 SECTION Q7 
 Please mark the extent to which you agree with the following statements about methadone.

 Number of complete cases =  169

Prompt: Please mark the extent to which you agree with the following statements about methadone.

Q7_1 md_replace

Q7_2 md_safe

Q7_3 md_side_eff

Q7_4 md_not_recov

Q7_5 md_bad_phys

Q7_6 md_get_high

Q7_7 md_cravings

Q7_8 md_from_high

(Q8) Buprenorphine

 SECTION Q8 
 Please mark the extent to which you agree with the following statements about oral buprenorphine (e.g. Suboxone, Subutex, Zubsolv, Bunavail).

 Number of complete cases =  113

Prompt: Please mark the extent to which you agree with the following statements about oral buprenorphine (e.g. Suboxone, Subutex, Zubsolv, Bunavail).

Q8_1 br_replace

Q8_2 br_safe

Q8_3 br_side_eff

Q8_4 br_not_recov

Q8_5 br_bad_phys

Q8_6 br_get_high

Q8_7 br_cravings

Q8_8 br_from_high

(Q9) Naltrexone

 SECTION Q9 
 Please mark the extent to which you agree with the following statements about extended-release naltrexone (e.g. Vivitrol).

 Number of complete cases =  110

Prompt: Please mark the extent to which you agree with the following statements about extended-release naltrexone (e.g. Vivitrol).

Q9_1 nt_replace

Q9_2 nt_safe

Q9_3 nt_side_eff

Q9_4 nt_not_recov

Q9_5 nt_bad_phys

Q9_6 nt_get_high

Q9_7 nt_cravings

Q9_8 nt_from_high

(Q15) Policy Support

 SECTION Q15 
 To what extent do you support each of the following policies?

 Number of complete cases =  703

Prompt: To what extent do you support each of the following policies?

Q15_1 needle

Q15_2 naloxone

Q15_3 safe_inject

Q15_4 heroin_maint

Q15_5 heroin_decrim

Q15_6 legal_testing

Q15_7 good_samar